Dear friends,
I’m delighted to announce that AI Fund has closed $190M for our new fund, in an oversubscribed round. I look forward to working with many more builders to create new companies that serve humanity.
AI Fund isn’t a traditional venture capital firm that invests in existing businesses. Instead, we are a venture builder (also called a venture studio): We co-found AI companies, so our team is directly involved in writing code, talking to customers to get feedback, iterating on product designs, preparing market analyses, and so on. We have a lot of fun building multiple AI products at a time, and thus live daily the emerging AI startup best practices.
Many factors go into the success of a startup. But if I had to pick just one, it would be speed. Startups live or die based on their ability to make good decisions and execute fast, which has been a recurring theme of my articles in The Batch as well.
If you are building an AI startup, here are some ideas to consider:
- A startup with a small team that pursues one focused, concrete idea can move really fast. Rather than hedging, it is often better to pursue one hypothesis (for example, build one concrete product) but also be willing to switch quickly to a different hypothesis (say, change what features you decide to build) if the data that comes back indicates the original hypothesis is flawed. Concreteness gets you speed!
- A subject matter expert’s gut is remarkably good at making quick decisions. Obviously, there’s a role for data and user studies as well. But if you’re deciding whether to build feature A or B, or to sell first to user persona X or Y, sometimes a domain expert’s gut will point to a quick decision that you can execute and validate or falsify. Trusting a domain expert’s gut gets you speed!
- AI-assisted coding is making prototyping faster than ever before. Yes, AI assistance is speeding up building reliable, enterprise-grade applications and maintaining legacy codebases. But the acceleration it brings to building stand-alone prototypes is far greater. This is because stand-alone prototypes have low requirements for reliability, integration, or even security (if, say, you run them in a sandbox environment). This lets us prototype and test at a ferocious velocity. AI -assisted coding (including vibe coding, where you might barely look at the code) gets you speed!
- Finally, with faster prototyping, the bottleneck shifts to getting feedback from users. A single learning cycle might consist of (i) building a prototype and (ii) getting user feedback to inform the next iteration. Since (i) is now much faster than before, accelerating (ii) is growing in importance. This means teams that are skilled at finding prospective customers and getting their feedback in hours/days rather than weeks can go faster. For example, when building consumer products, I routinely approach strangers (in a respectful way) in public places to ask if they’re willing to give feedback on a prototype I’m working on. (Gathering feedback is more complex for enterprise products, because prospective customers are harder to track down.) Quick user feedback gets you speed!
In addition to speed, a second criterion that I find important for startup success is deep knowledge of the technology. Because AI technology is evolving rapidly, a team with a deep technical understanding of what AI can and cannot do, and when to use what tool, will make better decisions. This creates meaningful differentiation and saves wasting time in blind alleys. A good technical understanding, too, gets you speed!
I’m grateful to AI Fund’s investors, team, and entrepreneur partners for working with us. There is much ahead to build!
Andrew